A discriminative approach for wavelet denoising.

This paper suggests a discriminative approach for wavelet denoising where a set of mapping functions (MFs) are applied to the transform coefficients in an attempt to produce a noise free image. As opposed to the descriptive approaches, modeling image or noise priors is not required here and the MFs are learned directly from an ensemble of example images using least-squares fitting. The suggested scheme generates a novel set of MFs that are essentially different from the traditional soft/hard thresholding in the over-complete case. These MFs are demonstrated to obtain comparable performance to the state-of-the-art denoising approaches. Additionally, this framework enables a seamless customization of the shrinkage operation to a new set of restoration problems that were not addressed previously with shrinkage techniques, such as deblurring, JPEG artifact removal, and various types of additive noise that are not necessarily Gaussian white noise.

A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur.

This paper addresses the problem of recovering a super-resolved image from a set of warped blurred and decimated versions thereof. Several algorithms have already been proposed for the solution of this general problem. In this paper, we concentrate on a special case where the warps are pure translations, the blur is space invariant and the same for all the images, and the noise is white. We exploit previous results to develop a new highly efficient super-resolution reconstruction algorithm for this case, which separates the treatment into de-blurring and measurements fusion. The fusion part is shown to be a very simple non-iterative algorithm, preserving the optimality of the entire reconstruction process, in the maximum-likelihood sense. Simulations demonstrate the capabilities of the proposed algorithm.

Texture fields and texture flows: sensitivity to differences.

There are two large classes of textures, those with an overall orientation structure (texture flows) and those without (texture fields). We investigate human sensitivity to detecting a patch of texture field within a texture flow psychophysically by using random not Moiré patterns. The resultant sensitivity, as a function of patch-size and path-length, is then related to a computational model of orientation selection, which reveals a connection between texture structure and the estimation of curvature. Finally, the connection back to curvature is confirmed by demonstrating a similarity between the patch sensitivity data and previous data on sensitivity to corners in flow patterns.